{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook takes a model trained on CIFAR-10 and gets ROC curve for OOD detection assuming SVHN to be the OOD dataset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "cwd = os.getcwd()\n",
    "module_path = \"/\".join(cwd.split('/')[0:-1])\n",
    "if module_path not in sys.path:\n",
    "    sys.path.append(module_path)\n",
    "import torch\n",
    "import random\n",
    "import matplotlib.pyplot as plt\n",
    "import torch.backends.cudnn as cudnn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import dataloaders\n",
    "import Data.cifar10 as cifar10\n",
    "import Data.svhn as svhn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import network architectures\n",
    "from Net.resnet import resnet50, resnet110\n",
    "from Net.wide_resnet import wide_resnet_cifar\n",
    "from Net.densenet import densenet121"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import metrics to compute\n",
    "from Metrics.ood_test_utils import get_roc_auc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import plot related libraries\n",
    "import seaborn as sb\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dataset params\n",
    "dataset_num_classes = {\n",
    "    'cifar10': 10,\n",
    "    'svhn': 10\n",
    "}\n",
    "\n",
    "dataset_loader = {\n",
    "    'cifar10': cifar10,\n",
    "    'svhn': svhn\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Mapping model name to model function\n",
    "models = {\n",
    "    'resnet50': resnet50,\n",
    "    'resnet110': resnet110,\n",
    "    'wide_resnet': wide_resnet_cifar,\n",
    "    'densenet121': densenet121,\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Checking if GPU is available\n",
    "cuda = False\n",
    "if (torch.cuda.is_available()):\n",
    "    cuda = True\n",
    "\n",
    "# Setting additional parameters\n",
    "torch.manual_seed(1)\n",
    "device = torch.device(\"cuda\" if cuda else \"cpu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "class args:\n",
    "    data_aug = True\n",
    "    gpu = device == \"cuda\"\n",
    "    train_batch_size = 128\n",
    "    test_batch_size = 128"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n",
      "Files already downloaded and verified\n",
      "Using downloaded and verified file: ./data/train_32x32.mat\n",
      "Using downloaded and verified file: ./data/train_32x32.mat\n",
      "Using downloaded and verified file: ./data/test_32x32.mat\n"
     ]
    }
   ],
   "source": [
    "dataset = 'cifar10'\n",
    "ood_dataset = 'svhn'\n",
    "\n",
    "num_classes = dataset_num_classes[dataset]\n",
    "train_loader, val_loader = dataset_loader[dataset].get_train_valid_loader(\n",
    "    batch_size=args.train_batch_size,\n",
    "    augment=args.data_aug,\n",
    "    random_seed=1,\n",
    "    pin_memory=args.gpu\n",
    ")\n",
    "\n",
    "test_loader = dataset_loader[dataset].get_test_loader(\n",
    "    batch_size=args.test_batch_size,\n",
    "    pin_memory=args.gpu\n",
    ")\n",
    "\n",
    "\n",
    "ood_train_loader, ood_val_loader = dataset_loader[ood_dataset].get_train_valid_loader(\n",
    "    batch_size=args.train_batch_size,\n",
    "    augment=args.data_aug,\n",
    "    random_seed=1,\n",
    "    pin_memory=args.gpu\n",
    ")\n",
    "\n",
    "ood_test_loader = dataset_loader[ood_dataset].get_test_loader(\n",
    "    batch_size=args.test_batch_size,\n",
    "    pin_memory=args.gpu\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter the model: \n",
      "resnet50\n",
      "Enter saved model name: \n",
      "resnet50_cross_entropy_350.model\n"
     ]
    }
   ],
   "source": [
    "# Taking input for the model\n",
    "print ('Enter the model: ')\n",
    "model_name = input()\n",
    "print ('Enter saved model name: ')\n",
    "saved_model_name = input()\n",
    "\n",
    "model = models[model_name]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "net = model(num_classes=num_classes, temp=1.0)\n",
    "net.cuda()\n",
    "net = torch.nn.DataParallel(net, device_ids=range(torch.cuda.device_count()))\n",
    "cudnn.benchmark = True\n",
    "net.load_state_dict(torch.load('./' + str(saved_model_name)))\n",
    "\n",
    "(fpr_entropy, tpr_entropy, thresholds_entropy), (fpr_confidence, tpr_confidence, thresholds_confidence), auc_entropy, auc_confidence = get_roc_auc(net, test_loader, ood_test_loader, device)"
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": 15,
=======
   "execution_count": 14,
>>>>>>> Modified README.md
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUROC entropy: 0.9433005646896128\n"
     ]
    },
    {
     "data": {
<<<<<<< HEAD
      "image/png": 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\n",
=======
      "image/png": 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\n",
>>>>>>> Modified README.md
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "clrs = ['#1f77b4','#ff7f0e', '#2ca02c','#d62728', '#9467bd', '#8c564b', '#e377c2', '#7f7f7f', '#bcbd22','#17becf']\n",
    "\n",
    "plt.figure()\n",
    "plt.rcParams[\"figure.figsize\"] = (10, 8)\n",
    "sb.set_style('whitegrid')\n",
    "plt.plot(fpr_entropy, tpr_entropy, color=clrs[0], linewidth=5, label='ROC')\n",
    "\n",
    "plt.xticks(fontsize=30)\n",
    "plt.yticks(fontsize=30)\n",
    "plt.xlabel('FPR', fontsize=30)\n",
    "plt.ylabel('TPR', fontsize=30)\n",
    "plt.legend(fontsize=28)\n",
    "        \n",
    "print ('AUROC entropy: ' + str(auc_entropy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
